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Learning a robot motor skill from scratch is impractically slow; so much so that in practice, learning must typically be bootstrapped using human demonstration. However, relying on human demonstration necessarily degrades the autonomy of robots that must learn a wide variety of skills over their operational lifetimes. We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation. We demonstrate the use of motion planners to bootstrap motor skills in two complex object manipulation scenarios with different policy representations: opening a drawer with a dynamic movement primitive representation, and closing a microwave door with a deep neural network policy. We also show how our method can bootstrap a motor skill for the challenging dynamic task of learning to hit a ball off a tee, where a kinematic plan based on treating the scene as static is insufficient to solve the task, but sufficient to bootstrap a more dynamic policy. In all three cases, our method is competitive with human-demonstrated initialization, and significantly outperforms starting with a random policy. This approach enables robots to to efficiently and autonomously learn motor policies for dynamic tasks without human demonstration.
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Learning Optimized Human Motion via Phase Space Analysis
This paper proposes a dynamic system based learning from demonstration approach to teach a robot activities of daily living. The approach takes inspiration from human movement literature to formulate trajectory learning as an optimal control problem.We assume a weighted combination of basis objective functions is the true objective function for a demonstrated motion. We derive basis objective functions analogous to those in human movement literature to optimize the robot’s motion. This method aims to naturally adapt the learned motion in different situations. To validate our approach, we learn motions from two categories: 1) commonly prescribed therapeutic exercises and 2) tea making. We show the reproduction accuracy of our method and compare torque requirements to the dynamic motion primitive for each motion, with and without an added load.
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- Award ID(s):
- 1830597
- PAR ID:
- 10227429
- Date Published:
- Journal Name:
- Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
- ISSN:
- 2153-0858
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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